{"list":"other","total":60,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"2004.11362","title":"Supervised Contrastive Learning","created":"2020-04-23","cited_by_count":2829},{"id":"1702.08734","title":"Billion-scale similarity search with GPUs","created":"2017-02-28","cited_by_count":2497},{"id":"1610.02136","title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","created":"2016-10-07","cited_by_count":2385},{"id":"1311.3618","title":"Describing Textures in the Wild","created":"2013-11-14","cited_by_count":2125},{"id":"1807.03888","title":"A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks","created":"2018-07-10","cited_by_count":1485},{"id":"1506.03365","title":"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop","created":"2015-06-10","cited_by_count":1355},{"id":"1706.02690","title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks","created":"2017-06-08","cited_by_count":1328},{"id":"2010.03759","title":"Energy-based Out-of-distribution Detection","created":"2020-10-08","cited_by_count":1299},{"id":"1812.11118","title":"Reconciling modern machine learning practice and the bias-variance trade-off","created":"2018-12-28","cited_by_count":927},{"id":"2110.11334","title":"Generalized Out-of-Distribution Detection: A Survey","created":"2021-10-21","cited_by_count":802},{"id":"2204.06507","title":"Out-of-Distribution Detection with Deep Nearest Neighbors","created":"2022-04-13","cited_by_count":600},{"id":"2111.12797","title":"ReAct: Out-of-distribution Detection With Rectified Activations","created":"2021-11-24","cited_by_count":486},{"id":"2102.03902","title":"Nystr\\\"omformer: A Nystr\\\"om-Based Algorithm for Approximating Self-Attention","created":"2021-02-07","cited_by_count":471},{"id":"1911.11132","title":"Scaling Out-of-Distribution Detection for Real-World Settings","created":"2019-11-25","cited_by_count":465},{"id":"1511.02222","title":"Deep Kernel Learning","created":"2015-11-06","cited_by_count":445},{"id":"2007.08176","title":"CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances","created":"2020-07-16","cited_by_count":441},{"id":"2002.11297","title":"Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data","created":"2020-02-26","cited_by_count":408},{"id":"2203.10807","title":"ViM: Out-Of-Distribution with Virtual-logit Matching","created":"2022-03-21","cited_by_count":405},{"id":"2110.00218","title":"On the Importance of Gradients for Detecting Distributional Shifts in the Wild","created":"2021-10-01","cited_by_count":339},{"id":"2103.12051","title":"SSD: A Unified Framework for Self-Supervised Outlier Detection","created":"2021-03-22","cited_by_count":264},{"id":"2209.09858","title":"Extremely Simple Activation Shaping for Out-of-Distribution Detection","created":"2022-09-20","cited_by_count":212},{"id":"2111.09805","title":"DICE: Leveraging Sparsification for Out-of-Distribution Detection","created":"2021-11-18","cited_by_count":166},{"id":"1504.06755","title":"TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking","created":"2015-04-25","cited_by_count":165},{"id":"2004.11154","title":"Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond","created":"2020-04-23","cited_by_count":109},{"id":"2210.14707","title":"Is Out-of-Distribution Detection Learnable?","created":"2022-10-26","cited_by_count":109},{"id":"2107.06641","title":"Trustworthy AI: A Computational Perspective","created":"2021-07-12","cited_by_count":93},{"id":"2308.14840","title":"Identifying and Mitigating the Security Risks of Generative AI","created":"2023-08-28","cited_by_count":91},{"id":"2112.00787","title":"Provable Guarantees for Understanding Out-of-distribution Detection","created":"2021-12-01","cited_by_count":78},{"id":"2310.00227","title":"Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement","created":"2023-09-30","cited_by_count":77},{"id":"2309.14888","title":"Nearest Neighbor Guidance for Out-of-Distribution Detection","created":"2023-09-26","cited_by_count":64},{"id":"2302.10326","title":"Unsupervised Out-of-Distribution Detection with Diffusion Inpainting","created":"2023-02-20","cited_by_count":59},{"id":"2209.08590","title":"RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection","created":"2022-09-18","cited_by_count":52},{"id":"2210.04200","title":"Boosting Out-of-distribution Detection with Typical Features","created":"2022-10-09","cited_by_count":48},{"id":"2303.13995","title":"LINe: Out-of-Distribution Detection by Leveraging Important Neurons","created":"2023-03-24","cited_by_count":34},{"id":"2212.02295","title":"Block Selection Method for Using Feature Norm in Out-of-distribution Detection","created":"2022-12-05","cited_by_count":30},{"id":"2402.02949","title":"Kernel PCA for Out-of-Distribution Detection","created":"2024-02-05","cited_by_count":24},{"id":"2208.10992","title":"Unsupervised Anomaly Localization with Structural Feature-Autoencoders","created":"2022-08-23","cited_by_count":23},{"id":"2308.00310","title":"GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients","created":"2023-08-01","cited_by_count":21},{"id":"2012.04250","title":"Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features","created":"2020-12-08","cited_by_count":20},{"id":"1402.0119","title":"Randomized Nonlinear Component Analysis","created":"2014-02-01","cited_by_count":18},{"id":"2302.11716","title":"VRA: Variational Rectified Activation for Out-of-distribution Detection","created":"2023-02-23","cited_by_count":15},{"id":"2404.12368","title":"Gradient-Regularized Out-of-Distribution Detection","created":"2024-04-18","cited_by_count":13},{"id":"1808.00934","title":"Streaming Kernel PCA with $\\tilde{O}(\\sqrt{n})$ Random Features","created":"2018-08-02","cited_by_count":11},{"id":"2105.08875","title":"Statistical Optimality and Computational Efficiency of Nystr\\\"om Kernel PCA","created":"2021-05-19","cited_by_count":10},{"id":"2310.14227","title":"Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective","created":"2023-10-22","cited_by_count":10},{"id":"2112.06384","title":"WOOD: Wasserstein-based Out-of-Distribution Detection","created":"2021-12-13","cited_by_count":9},{"id":"2303.07543","title":"WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis","created":"2023-03-14","cited_by_count":9},{"id":"2302.09726","title":"Nystrom Method for Accurate and Scalable Implicit Differentiation","created":"2023-02-20","cited_by_count":8},{"id":"2009.04614","title":"End-to-end Kernel Learning via Generative Random Fourier Features","created":"2020-09-10","cited_by_count":7},{"id":"2310.17163","title":"Low-Dimensional Gradient Helps Out-of-Distribution Detection","created":"2023-10-26","cited_by_count":6},{"id":"2206.09380","title":"Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection","created":"2022-06-19","cited_by_count":4},{"id":"2406.08748","title":"Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nystr\\\"om method","created":"2024-06-13","cited_by_count":3},{"id":"2109.05578","title":"Kernel PCA with the Nystr\\\"om method","created":"2021-09-12","cited_by_count":2},{"id":"2311.13959","title":"RankFeat&RankWeight: Rank-1 Feature/Weight Removal for Out-of-distribution Detection","created":"2023-11-23","cited_by_count":1}],"next_cursor":null}